A Spatial Econometric Approach to the Economics of Site‐Specific Nitrogen Management in Corn Production

Anselin, Luc; Bongiovanni, Rodolfo; Lowenberg‐DeBoer, James

doi:10.1111/j.0002-9092.2004.00610.x

Cited by 153 publications

(134 citation statements)

References 17 publications

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“…Therefore, the study follows the method generally proclaimed in the literature by introducing the data of the spatial weight matrix to verify the spatial autocorrelation of the panel data. Spatial autocorrelation can be measured through the spatial autocorrelation index Moran's I [32][33][34]; this study adopts "Local Moran' s I". The calculation formula reads as follows:…”

Section: Moran Indexmentioning

confidence: 99%

Economic Transformation in the Beijing-Tianjin-Hebei Region: Is It Undergoing the Environmental Kuznets Curve?

Xiong

Jong

et al. 2017

Sustainability

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Abstract:The Beijing-Tianjin-Hebei Region Integration Plan is one of the most important national strategies in China promoting regional economic development. The environmental problems in this region, however, especially air pollution and contaminated groundwater, have enormous influence on the people's health while also causing economic loss. Therefore, this study aims to analyze the pattern of its environmental and economic development. Panel data in the period 2004-2014 are used to establish an advanced model of the Environmental Kuznets Curve. The results indicate that the economic growth and environmental pollution of Beijing-Tianjin-Hebei region do not completely meet the Environment Kuznets Curve assumptions. The discharge volume of industrial wastewater and economic growth reflect a wave-type relation. The sulfur dioxide discharge volume and economic growth reflect a U-shaped relation; the generated volume of industrial solid wastes and economic growth reflect a reversed N-shaped relation, which is in accordance with the Environmental Kuznets Curve characteristics at the second inflection point. The variables added value of the secondary industry, population size and raw coal consumption volume have a significant positive influence on the discharge of various environmental pollutants in Beijing-Tianjin-Hebei region. The analysis provides policy recommendations for the government to develop regional economic and environmental protection policies.

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Section: Moran Indexmentioning

confidence: 99%

Economic Transformation in the Beijing-Tianjin-Hebei Region: Is It Undergoing the Environmental Kuznets Curve?

Xiong

Jong

et al. 2017

Sustainability

View full text Add to dashboard Cite

show abstract

“…These models did not take spatial autocorrelation structure into account, which, in turn, affects the crops site-specific function estimation, leading to inflated variance and most likely wrong conclusions. As demonstrated by Anselin [24], spatial correlation of regression residuals should be critically considered in the analysis of yield monitor data. Following this rationale, the application of spatial econometric methods incorporates a simultaneous autoregressive model of order one for the error term (SAR error model or SPL) and considers spatial neighborhood dependence structure [24][25][26].…”

mentioning

confidence: 99%

Mid-Season High-Resolution Satellite Imagery for Forecasting Site-Specific Corn Yield

Peralta

Assefa

et al. 2016

Remote Sensing

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A timely and accurate crop yield forecast is crucial to make better decisions on crop management, marketing, and storage by assessing ahead and implementing based on expected crop performance. The objective of this study was to investigate the potential of high-resolution satellite imagery data collected at mid-growing season for identification of within-field variability and to forecast corn yield at different sites within a field. A test was conducted on yield monitor data and RapidEye satellite imagery obtained for 22 cornfields located in five different counties (Clay, Dickinson, Rice, Saline, and Washington) of Kansas (total of 457 ha). Three basic tests were conducted on the data: (1) spatial dependence on each of the yield and vegetation indices (VIs) using Moran's I test; (2) model selection for the relationship between imagery data and actual yield using ordinary least square regression (OLS) and spatial econometric (SPL) models; and (3) model validation for yield forecasting purposes. Spatial autocorrelation analysis (Moran's I test) for both yield and VIs (red edge NDVI = NDVIre, normalized difference vegetation index = NDVIr, SRre = red-edge simple ratio, near infrared = NIR and green-NDVI = NDVIG) was tested positive and statistically significant for most of the fields (p < 0.05), except for one. Inclusion of spatial adjustment to model improved the model fit on most fields as compared to OLS models, with the spatial adjustment coefficient significant for half of the fields studied. When selected models were used for prediction to validate dataset, a striking similarity (RMSE = 0.02) was obtained between predicted and observed yield within a field. Yield maps could assist implementing more effective site-specific management tools and could be utilized as a proxy of yield monitor data. In summary, high-resolution satellite imagery data can be reasonably used to forecast yield via utilization of models that include spatial adjustment to inform precision agricultural management decisions.

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“…Spatial analysis is an inferential spatial statistical technique that explicitly accounts for spatial heterogeneity as well as the spatial interaction structure of neighboring observations. Spatial analysis techniques have been applied to site-specific yield monitor data before (Anselin et al 2004;Griffin et al 2005Griffin et al , 2006bLambert et al 2004;Hurley et al 2005). Three farmers conducted five field-scale on-farm trials and received spatial analysis reports.…”

Section: Methodsmentioning

confidence: 99%

Spatial analysis of yield monitor data: case studies of on-farm trials and farm management decision making

et al. 2008

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A 3-year case study was undertaken of how North American farmers use yield monitors for on-farm trials in farm management decision making. Case study methods were used because relatively few farmers quantitatively analyze yield monitor data. At this early research stage, insufficient farm management information about the data was available to ask the right questions in a large-scale survey. In addition to the formal case study of farmers experienced at using yield monitors to collect on-farm trial data, the study evaluated the effect of yield monitor data quality on farm decisions. Two levels of yield data quality included standard output where the default settings of farm-level mapping software were accepted and where filtering of the data was undertaken. Results indicated that yield data quality affects farm management decisions. In addition, farmers receiving a spatial analysis of their on-farm trial data tended to use split-field designs instead of replicated split-planter designs. They were also more confident in their decisions than before participation in the spatial analysis project, and made decisions more quickly.

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A Spatial Econometric Approach to the Economics of Site‐Specific Nitrogen Management in Corn Production

Cited by 153 publications

References 17 publications

Economic Transformation in the Beijing-Tianjin-Hebei Region: Is It Undergoing the Environmental Kuznets Curve?

Economic Transformation in the Beijing-Tianjin-Hebei Region: Is It Undergoing the Environmental Kuznets Curve?

Mid-Season High-Resolution Satellite Imagery for Forecasting Site-Specific Corn Yield

Spatial analysis of yield monitor data: case studies of on-farm trials and farm management decision making

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